Commodifying infrastructure spatial dynamics with crowdsourced smartphone data

Structural information deficits about our aging bridges have led to several avoidable catastrophes in recent years. Data-driven methods for bridge vibration monitoring enable frequent, accurate structural assessments; however, the high costs of large-scale deployments of these systems make important condition information a luxury for bridge owners. Smartphone-based monitoring is inexpensive yet has produced structural information, i.e., modal frequencies, in crowdsensing applications. However, current methods cannot extract spatial vibration characteristics, which are needed for damage identification. Here we present the most extensive real-world study on bridge monitoring with crowdsourced smartphone-vehicle trips and simulate damage detection capabilities. Our method analyzes over 500 trips across four bridges with main spans ranging from 30 to 1300 meters in length, representing about one-quarter of US bridges, and extracts absolute value mode shapes, a damage-sensitive feature. We d..., This data set was collected from various sources: the research team, ANAS employees, and Uber drivers. The method for data collection and data processing for each dataset can be found in the related works. , , # Commodifying infrastructure spatial dynamics with crowdsourced smartphone data

[https://doi.org/10.5061/dryad.15dv41p49](https://doi.org/10.5061/dryad.15dv41p49)

The dataset consists of acceleration and position measurements taken using smartphones as vehicles drove across various bridges. Current projects using this data revolve around mobile sensing and system identification. The accelerations were measured using the smartphone's onboard triaxial accelerometer. However, the position measurements are a process version of the GPS measurements. The position measurements are 1D distance from a bridge pier, measured in meters, rather than the raw 2D GPS measurements from the phone. Within this repository, there are multiple case studies, and between cases, the method of data collection varied. The methods for data collection can be found in the related work.  A Jupyter Notebook accompanies the datasets to reproduce the Golden Gate Bridge absolute mode shape results in the related work....